MG
M. Guo
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Accurate state of charge (SOC) estimation is essential for battery management systems (BMSs) in heavy-duty vehicles (HDVs), where batteries are exposed to high voltage and current stress, heavy loads, and dynamic operating conditions. In the context of company-oriented BMS development, the SOC estimator should be accurate, suitable for real-time embedded implementation, scalable to large battery systems, and transparent enough for engineering validation. Lithium iron phosphate (LFP) batteries are attractive in HDV applications because of their safety, relatively low cost, and long cycle life, but the long plateau region in the voltage--SOC curve limits the reliability of voltage-based SOC correction and calibration. For this reason, Coulomb Counting (CC) is selected as the basic SOC propagation framework in this thesis. CC is practical and industrially relevant because it directly uses measured current and has low computational burden, but its sensitivity to initial SOC, current measurement error, and battery capacity mismatch creates challenges for reliable BMS implementation.
The thesis develops a Simulink-based benchmark framework to evaluate selected CC-based SOC estimation and correction strategies for LFP batteries under heavy-duty vehicle operating conditions. A conventional CC estimator, two literature-based correction methods, an improved bias-compensation strategy, and a bias-capacity-aware correction method developed in this thesis are implemented and compared under controlled error cases. The results show that no single correction mechanism is optimal under all conditions: initial SOC correction depends on voltage observability, cycle-level capacity correction is more effective under capacity mismatch, and current-bias compensation is necessary when current sensor drift causes accumulated SOC error. These findings demonstrate that reliable CC enhancement for LFP-based heavy-duty battery systems requires not a stronger correction mechanism, but one matched to the dominant error source and constrained by the reliability of the available correction signals. The benchmark provides an early-stage evaluation platform for future company BMS development, supporting the selection of correction strategies before experimental validation and embedded implementation. ...
The thesis develops a Simulink-based benchmark framework to evaluate selected CC-based SOC estimation and correction strategies for LFP batteries under heavy-duty vehicle operating conditions. A conventional CC estimator, two literature-based correction methods, an improved bias-compensation strategy, and a bias-capacity-aware correction method developed in this thesis are implemented and compared under controlled error cases. The results show that no single correction mechanism is optimal under all conditions: initial SOC correction depends on voltage observability, cycle-level capacity correction is more effective under capacity mismatch, and current-bias compensation is necessary when current sensor drift causes accumulated SOC error. These findings demonstrate that reliable CC enhancement for LFP-based heavy-duty battery systems requires not a stronger correction mechanism, but one matched to the dominant error source and constrained by the reliability of the available correction signals. The benchmark provides an early-stage evaluation platform for future company BMS development, supporting the selection of correction strategies before experimental validation and embedded implementation. ...
Accurate state of charge (SOC) estimation is essential for battery management systems (BMSs) in heavy-duty vehicles (HDVs), where batteries are exposed to high voltage and current stress, heavy loads, and dynamic operating conditions. In the context of company-oriented BMS development, the SOC estimator should be accurate, suitable for real-time embedded implementation, scalable to large battery systems, and transparent enough for engineering validation. Lithium iron phosphate (LFP) batteries are attractive in HDV applications because of their safety, relatively low cost, and long cycle life, but the long plateau region in the voltage--SOC curve limits the reliability of voltage-based SOC correction and calibration. For this reason, Coulomb Counting (CC) is selected as the basic SOC propagation framework in this thesis. CC is practical and industrially relevant because it directly uses measured current and has low computational burden, but its sensitivity to initial SOC, current measurement error, and battery capacity mismatch creates challenges for reliable BMS implementation.
The thesis develops a Simulink-based benchmark framework to evaluate selected CC-based SOC estimation and correction strategies for LFP batteries under heavy-duty vehicle operating conditions. A conventional CC estimator, two literature-based correction methods, an improved bias-compensation strategy, and a bias-capacity-aware correction method developed in this thesis are implemented and compared under controlled error cases. The results show that no single correction mechanism is optimal under all conditions: initial SOC correction depends on voltage observability, cycle-level capacity correction is more effective under capacity mismatch, and current-bias compensation is necessary when current sensor drift causes accumulated SOC error. These findings demonstrate that reliable CC enhancement for LFP-based heavy-duty battery systems requires not a stronger correction mechanism, but one matched to the dominant error source and constrained by the reliability of the available correction signals. The benchmark provides an early-stage evaluation platform for future company BMS development, supporting the selection of correction strategies before experimental validation and embedded implementation.
The thesis develops a Simulink-based benchmark framework to evaluate selected CC-based SOC estimation and correction strategies for LFP batteries under heavy-duty vehicle operating conditions. A conventional CC estimator, two literature-based correction methods, an improved bias-compensation strategy, and a bias-capacity-aware correction method developed in this thesis are implemented and compared under controlled error cases. The results show that no single correction mechanism is optimal under all conditions: initial SOC correction depends on voltage observability, cycle-level capacity correction is more effective under capacity mismatch, and current-bias compensation is necessary when current sensor drift causes accumulated SOC error. These findings demonstrate that reliable CC enhancement for LFP-based heavy-duty battery systems requires not a stronger correction mechanism, but one matched to the dominant error source and constrained by the reliability of the available correction signals. The benchmark provides an early-stage evaluation platform for future company BMS development, supporting the selection of correction strategies before experimental validation and embedded implementation.